Overview

Dataset statistics

Number of variables18
Number of observations7000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory984.5 KiB
Average record size in memory144.0 B

Variable types

Categorical4
Numeric14

Alerts

ID has a high cardinality: 7000 distinct valuesHigh cardinality
키(cm) is highly overall correlated with 몸무게(kg) and 1 other fieldsHigh correlation
몸무게(kg) is highly overall correlated with 키(cm) and 2 other fieldsHigh correlation
BMI is highly overall correlated with 몸무게(kg)High correlation
콜레스테롤 is highly overall correlated with 저밀도지단백High correlation
저밀도지단백 is highly overall correlated with 콜레스테롤High correlation
헤모글로빈 is highly overall correlated with 키(cm) and 1 other fieldsHigh correlation
요 단백 is highly imbalanced (83.5%)Imbalance
ID is uniformly distributedUniform
ID has unique valuesUnique

Reproduction

Analysis started2023-08-17 07:11:04.855388
Analysis finished2023-08-17 07:11:56.011661
Duration51.16 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

ID
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct7000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
TRAIN_0000
 
1
TRAIN_4663
 
1
TRAIN_4674
 
1
TRAIN_4673
 
1
TRAIN_4672
 
1
Other values (6995)
6995 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters70000
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7000 ?
Unique (%)100.0%

Sample

1st rowTRAIN_0000
2nd rowTRAIN_0001
3rd rowTRAIN_0002
4th rowTRAIN_0003
5th rowTRAIN_0004

Common Values

ValueCountFrequency (%)
TRAIN_0000 1
 
< 0.1%
TRAIN_4663 1
 
< 0.1%
TRAIN_4674 1
 
< 0.1%
TRAIN_4673 1
 
< 0.1%
TRAIN_4672 1
 
< 0.1%
TRAIN_4671 1
 
< 0.1%
TRAIN_4670 1
 
< 0.1%
TRAIN_4669 1
 
< 0.1%
TRAIN_4668 1
 
< 0.1%
TRAIN_4667 1
 
< 0.1%
Other values (6990) 6990
99.9%

Length

2023-08-17T07:11:56.188806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
train_0000 1
 
< 0.1%
train_0013 1
 
< 0.1%
train_0003 1
 
< 0.1%
train_0004 1
 
< 0.1%
train_0005 1
 
< 0.1%
train_0006 1
 
< 0.1%
train_0007 1
 
< 0.1%
train_0008 1
 
< 0.1%
train_0009 1
 
< 0.1%
train_0010 1
 
< 0.1%
Other values (6990) 6990
99.9%

Most occurring characters

ValueCountFrequency (%)
T 7000
10.0%
R 7000
10.0%
A 7000
10.0%
I 7000
10.0%
N 7000
10.0%
_ 7000
10.0%
0 3100
 
4.4%
4 3100
 
4.4%
6 3100
 
4.4%
3 3100
 
4.4%
Other values (6) 15600
22.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 35000
50.0%
Decimal Number 28000
40.0%
Connector Punctuation 7000
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3100
11.1%
4 3100
11.1%
6 3100
11.1%
3 3100
11.1%
2 3100
11.1%
1 3100
11.1%
5 3100
11.1%
7 2100
7.5%
9 2100
7.5%
8 2100
7.5%
Uppercase Letter
ValueCountFrequency (%)
T 7000
20.0%
R 7000
20.0%
A 7000
20.0%
I 7000
20.0%
N 7000
20.0%
Connector Punctuation
ValueCountFrequency (%)
_ 7000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 35000
50.0%
Common 35000
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 7000
20.0%
0 3100
8.9%
4 3100
8.9%
6 3100
8.9%
3 3100
8.9%
2 3100
8.9%
1 3100
8.9%
5 3100
8.9%
7 2100
 
6.0%
9 2100
 
6.0%
Latin
ValueCountFrequency (%)
T 7000
20.0%
R 7000
20.0%
A 7000
20.0%
I 7000
20.0%
N 7000
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 7000
10.0%
R 7000
10.0%
A 7000
10.0%
I 7000
10.0%
N 7000
10.0%
_ 7000
10.0%
0 3100
 
4.4%
4 3100
 
4.4%
6 3100
 
4.4%
3 3100
 
4.4%
Other values (6) 15600
22.3%

나이
Real number (ℝ)

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.973571
Minimum20
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2023-08-17T07:11:56.392930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile25
Q135
median40
Q350
95-th percentile65
Maximum85
Range65
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.063793
Coefficient of variation (CV)0.27434189
Kurtosis-0.14377817
Mean43.973571
Median Absolute Deviation (MAD)10
Skewness0.2919904
Sum307815
Variance145.53509
MonotonicityNot monotonic
2023-08-17T07:11:56.581656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
40 1904
27.2%
45 902
12.9%
60 726
 
10.4%
50 682
 
9.7%
55 623
 
8.9%
35 580
 
8.3%
30 532
 
7.6%
25 456
 
6.5%
20 201
 
2.9%
65 168
 
2.4%
Other values (4) 226
 
3.2%
ValueCountFrequency (%)
20 201
 
2.9%
25 456
 
6.5%
30 532
 
7.6%
35 580
 
8.3%
40 1904
27.2%
45 902
12.9%
50 682
 
9.7%
55 623
 
8.9%
60 726
 
10.4%
65 168
 
2.4%
ValueCountFrequency (%)
85 3
 
< 0.1%
80 29
 
0.4%
75 77
 
1.1%
70 117
 
1.7%
65 168
 
2.4%
60 726
 
10.4%
55 623
 
8.9%
50 682
 
9.7%
45 902
12.9%
40 1904
27.2%

키(cm)
Real number (ℝ)

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.78143
Minimum135
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2023-08-17T07:11:56.789465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum135
5-th percentile150
Q1160
median165
Q3170
95-th percentile180
Maximum190
Range55
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.1702133
Coefficient of variation (CV)0.055650769
Kurtosis-0.60305679
Mean164.78143
Median Absolute Deviation (MAD)5
Skewness-0.17587909
Sum1153470
Variance84.092811
MonotonicityNot monotonic
2023-08-17T07:11:56.954689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
170 1414
20.2%
165 1248
17.8%
160 1121
16.0%
175 1064
15.2%
155 952
13.6%
150 538
 
7.7%
180 404
 
5.8%
145 144
 
2.1%
185 79
 
1.1%
140 33
 
0.5%
Other values (2) 3
 
< 0.1%
ValueCountFrequency (%)
135 1
 
< 0.1%
140 33
 
0.5%
145 144
 
2.1%
150 538
 
7.7%
155 952
13.6%
160 1121
16.0%
165 1248
17.8%
170 1414
20.2%
175 1064
15.2%
180 404
 
5.8%
ValueCountFrequency (%)
190 2
 
< 0.1%
185 79
 
1.1%
180 404
 
5.8%
175 1064
15.2%
170 1414
20.2%
165 1248
17.8%
160 1121
16.0%
155 952
13.6%
150 538
 
7.7%
145 144
 
2.1%

몸무게(kg)
Real number (ℝ)

Distinct21
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.932857
Minimum30
Maximum130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2023-08-17T07:11:57.166296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile45
Q155
median65
Q375
95-th percentile90
Maximum130
Range100
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.978702
Coefficient of variation (CV)0.19684725
Kurtosis0.41951061
Mean65.932857
Median Absolute Deviation (MAD)10
Skewness0.55559457
Sum461530
Variance168.4467
MonotonicityNot monotonic
2023-08-17T07:11:57.379998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
70 1017
14.5%
65 1002
14.3%
60 974
13.9%
55 954
13.6%
75 762
10.9%
50 692
9.9%
80 520
7.4%
45 293
 
4.2%
85 292
 
4.2%
90 195
 
2.8%
Other values (11) 299
 
4.3%
ValueCountFrequency (%)
30 1
 
< 0.1%
35 10
 
0.1%
40 63
 
0.9%
45 293
 
4.2%
50 692
9.9%
55 954
13.6%
60 974
13.9%
65 1002
14.3%
70 1017
14.5%
75 762
10.9%
ValueCountFrequency (%)
130 1
 
< 0.1%
125 3
 
< 0.1%
120 4
 
0.1%
115 6
 
0.1%
110 11
 
0.2%
105 29
 
0.4%
100 64
 
0.9%
95 107
 
1.5%
90 195
2.8%
85 292
4.2%

BMI
Real number (ℝ)

Distinct117
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.144423
Minimum14.27
Maximum42.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2023-08-17T07:11:57.594866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum14.27
5-th percentile19.02
Q121.6
median23.88
Q326.12
95-th percentile30.86
Maximum42.45
Range28.18
Interquartile range (IQR)4.52

Descriptive statistics

Standard deviation3.5019451
Coefficient of variation (CV)0.14504157
Kurtosis0.8589093
Mean24.144423
Median Absolute Deviation (MAD)2.24
Skewness0.61165357
Sum169010.96
Variance12.263619
MonotonicityNot monotonic
2023-08-17T07:11:57.848526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.22 313
 
4.5%
23.88 279
 
4.0%
22.49 276
 
3.9%
21.48 269
 
3.8%
22.89 252
 
3.6%
22.04 248
 
3.5%
23.44 246
 
3.5%
25.95 238
 
3.4%
22.86 236
 
3.4%
25.71 232
 
3.3%
Other values (107) 4411
63.0%
ValueCountFrequency (%)
14.27 1
 
< 0.1%
15.56 3
 
< 0.1%
15.57 4
 
0.1%
15.62 5
 
0.1%
16.33 6
 
0.1%
16.53 10
 
0.1%
16.65 27
0.4%
17.3 18
 
0.3%
17.53 1
 
< 0.1%
17.58 52
0.7%
ValueCountFrequency (%)
42.45 1
 
< 0.1%
41.62 1
 
< 0.1%
40.82 3
< 0.1%
39.18 2
 
< 0.1%
38.06 2
 
< 0.1%
37.78 1
 
< 0.1%
37.55 3
< 0.1%
37.46 2
 
< 0.1%
37.04 1
 
< 0.1%
36.73 5
0.1%

시력
Real number (ℝ)

Distinct49
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.01165
Minimum0.1
Maximum9.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2023-08-17T07:11:58.114286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q10.8
median1
Q31.2
95-th percentile1.5
Maximum9.9
Range9.8
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.42782848
Coefficient of variation (CV)0.42290168
Kurtosis72.715143
Mean1.01165
Median Absolute Deviation (MAD)0.2
Skewness5.8126143
Sum7081.55
Variance0.18303721
MonotonicityNot monotonic
2023-08-17T07:11:58.364347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1.2 801
11.4%
1 751
 
10.7%
1.1 748
 
10.7%
1.5 507
 
7.2%
1.35 496
 
7.1%
0.95 440
 
6.3%
0.9 432
 
6.2%
0.8 326
 
4.7%
0.75 273
 
3.9%
0.7 264
 
3.8%
Other values (39) 1962
28.0%
ValueCountFrequency (%)
0.1 14
 
0.2%
0.15 6
 
0.1%
0.2 25
 
0.4%
0.25 26
 
0.4%
0.3 39
0.6%
0.35 60
0.9%
0.4 2
 
< 0.1%
0.4 78
1.1%
0.45 28
 
0.4%
0.45 65
0.9%
ValueCountFrequency (%)
9.9 1
 
< 0.1%
5.95 1
 
< 0.1%
5.7 6
0.1%
5.55 3
 
< 0.1%
5.45 8
0.1%
5.35 3
 
< 0.1%
5.3 3
 
< 0.1%
5.25 1
 
< 0.1%
5.1 1
 
< 0.1%
5.05 1
 
< 0.1%

충치
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
0
5408 
1
1592 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5408
77.3%
1 1592
 
22.7%

Length

2023-08-17T07:11:59.152165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-17T07:11:59.541977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 5408
77.3%
1 1592
 
22.7%

Most occurring characters

ValueCountFrequency (%)
0 5408
77.3%
1 1592
 
22.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5408
77.3%
1 1592
 
22.7%

Most occurring scripts

ValueCountFrequency (%)
Common 7000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5408
77.3%
1 1592
 
22.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5408
77.3%
1 1592
 
22.7%

공복 혈당
Real number (ℝ)

Distinct172
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.331857
Minimum57
Maximum386
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2023-08-17T07:11:59.873137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum57
5-th percentile80
Q189
median96
Q3104
95-th percentile129
Maximum386
Range329
Interquartile range (IQR)15

Descriptive statistics

Standard deviation21.12967
Coefficient of variation (CV)0.21271796
Kurtosis33.489727
Mean99.331857
Median Absolute Deviation (MAD)7
Skewness4.5023756
Sum695323
Variance446.46294
MonotonicityNot monotonic
2023-08-17T07:12:00.236311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97 279
 
4.0%
92 270
 
3.9%
94 268
 
3.8%
98 266
 
3.8%
95 263
 
3.8%
90 262
 
3.7%
93 253
 
3.6%
96 252
 
3.6%
99 244
 
3.5%
91 242
 
3.5%
Other values (162) 4401
62.9%
ValueCountFrequency (%)
57 1
 
< 0.1%
60 1
 
< 0.1%
61 2
 
< 0.1%
64 1
 
< 0.1%
65 6
0.1%
66 1
 
< 0.1%
67 4
 
0.1%
68 2
 
< 0.1%
69 2
 
< 0.1%
70 11
0.2%
ValueCountFrequency (%)
386 1
< 0.1%
375 1
< 0.1%
342 1
< 0.1%
314 1
< 0.1%
313 1
< 0.1%
308 1
< 0.1%
302 1
< 0.1%
290 1
< 0.1%
288 1
< 0.1%
285 1
< 0.1%

혈압
Real number (ℝ)

Distinct70
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.532857
Minimum14
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2023-08-17T07:12:00.596500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile32
Q140
median45
Q350
95-th percentile61
Maximum91
Range77
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.820611
Coefficient of variation (CV)0.19371969
Kurtosis0.84124374
Mean45.532857
Median Absolute Deviation (MAD)5
Skewness0.48571352
Sum318730
Variance77.803178
MonotonicityNot monotonic
2023-08-17T07:12:01.042881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 870
 
12.4%
50 618
 
8.8%
42 389
 
5.6%
44 347
 
5.0%
48 310
 
4.4%
46 310
 
4.4%
38 238
 
3.4%
45 238
 
3.4%
52 231
 
3.3%
43 217
 
3.1%
Other values (60) 3232
46.2%
ValueCountFrequency (%)
14 1
 
< 0.1%
17 2
 
< 0.1%
19 1
 
< 0.1%
20 7
0.1%
21 4
 
0.1%
22 3
 
< 0.1%
23 4
 
0.1%
24 6
0.1%
25 9
0.1%
26 11
0.2%
ValueCountFrequency (%)
91 1
 
< 0.1%
89 1
 
< 0.1%
88 1
 
< 0.1%
85 1
 
< 0.1%
82 5
0.1%
81 1
 
< 0.1%
80 4
0.1%
79 3
< 0.1%
78 2
 
< 0.1%
77 3
< 0.1%

중성 지방
Real number (ℝ)

Distinct371
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.14471
Minimum21
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2023-08-17T07:12:01.473834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile46
Q174
median107
Q3161
95-th percentile283
Maximum999
Range978
Interquartile range (IQR)87

Descriptive statistics

Standard deviation73.918492
Coefficient of variation (CV)0.58137291
Kurtosis4.0674762
Mean127.14471
Median Absolute Deviation (MAD)39
Skewness1.491658
Sum890013
Variance5463.9435
MonotonicityNot monotonic
2023-08-17T07:12:01.887235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71 81
 
1.2%
58 77
 
1.1%
82 74
 
1.1%
83 72
 
1.0%
80 69
 
1.0%
70 67
 
1.0%
77 66
 
0.9%
68 64
 
0.9%
86 64
 
0.9%
78 64
 
0.9%
Other values (361) 6302
90.0%
ValueCountFrequency (%)
21 1
 
< 0.1%
23 3
 
< 0.1%
25 4
 
0.1%
26 1
 
< 0.1%
27 7
0.1%
28 6
0.1%
29 11
0.2%
30 8
0.1%
31 11
0.2%
32 8
0.1%
ValueCountFrequency (%)
999 1
 
< 0.1%
399 1
 
< 0.1%
398 1
 
< 0.1%
397 6
0.1%
396 1
 
< 0.1%
394 3
< 0.1%
393 2
 
< 0.1%
391 3
< 0.1%
389 1
 
< 0.1%
388 3
< 0.1%

혈청 크레아티닌
Real number (ℝ)

Distinct23
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8849
Minimum0.1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2023-08-17T07:12:02.292903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q10.8
median0.9
Q31
95-th percentile1.2
Maximum10
Range9.9
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.24152263
Coefficient of variation (CV)0.27293777
Kurtosis391.41776
Mean0.8849
Median Absolute Deviation (MAD)0.1
Skewness11.957205
Sum6194.3
Variance0.05833318
MonotonicityNot monotonic
2023-08-17T07:12:02.668648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.9 1449
20.7%
0.8 1314
18.8%
1 1245
17.8%
0.7 966
13.8%
1.1 707
10.1%
0.6 550
 
7.9%
1.2 374
 
5.3%
0.5 201
 
2.9%
1.3 99
 
1.4%
1.4 39
 
0.6%
Other values (13) 56
 
0.8%
ValueCountFrequency (%)
0.1 2
 
< 0.1%
0.3 1
 
< 0.1%
0.4 24
 
0.3%
0.5 201
 
2.9%
0.6 550
 
7.9%
0.7 966
13.8%
0.8 1314
18.8%
0.9 1449
20.7%
1 1245
17.8%
1.1 707
10.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
7.4 1
 
< 0.1%
5.9 1
 
< 0.1%
2.6 1
 
< 0.1%
2.5 1
 
< 0.1%
2 3
 
< 0.1%
1.9 1
 
< 0.1%
1.7 2
 
< 0.1%
1.6 7
0.1%
1.5 11
0.2%

콜레스테롤
Real number (ℝ)

Distinct232
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean197.27657
Minimum86
Maximum395
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2023-08-17T07:12:03.046789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum86
5-th percentile142
Q1173
median195
Q3219
95-th percentile259
Maximum395
Range309
Interquartile range (IQR)46

Descriptive statistics

Standard deviation36.306494
Coefficient of variation (CV)0.18403855
Kurtosis0.71887429
Mean197.27657
Median Absolute Deviation (MAD)23
Skewness0.41905168
Sum1380936
Variance1318.1615
MonotonicityNot monotonic
2023-08-17T07:12:03.290967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
185 109
 
1.6%
193 97
 
1.4%
216 92
 
1.3%
187 91
 
1.3%
210 90
 
1.3%
201 87
 
1.2%
199 86
 
1.2%
207 86
 
1.2%
182 85
 
1.2%
186 84
 
1.2%
Other values (222) 6093
87.0%
ValueCountFrequency (%)
86 1
 
< 0.1%
95 1
 
< 0.1%
97 1
 
< 0.1%
98 4
0.1%
100 1
 
< 0.1%
101 2
 
< 0.1%
102 2
 
< 0.1%
103 1
 
< 0.1%
104 1
 
< 0.1%
106 6
0.1%
ValueCountFrequency (%)
395 1
< 0.1%
386 1
< 0.1%
380 1
< 0.1%
375 1
< 0.1%
369 1
< 0.1%
366 1
< 0.1%
363 1
< 0.1%
351 1
< 0.1%
338 1
< 0.1%
333 1
< 0.1%

고밀도지단백
Real number (ℝ)

Distinct104
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.355429
Minimum18
Maximum157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2023-08-17T07:12:03.539916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile37
Q147
median55
Q366
95-th percentile83
Maximum157
Range139
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.506945
Coefficient of variation (CV)0.25293063
Kurtosis1.42488
Mean57.355429
Median Absolute Deviation (MAD)9
Skewness0.85271567
Sum401488
Variance210.45145
MonotonicityNot monotonic
2023-08-17T07:12:03.784672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 236
 
3.4%
48 214
 
3.1%
51 212
 
3.0%
54 210
 
3.0%
58 207
 
3.0%
47 205
 
2.9%
55 202
 
2.9%
49 198
 
2.8%
52 190
 
2.7%
56 189
 
2.7%
Other values (94) 4937
70.5%
ValueCountFrequency (%)
18 1
 
< 0.1%
22 1
 
< 0.1%
24 4
 
0.1%
25 2
 
< 0.1%
26 3
 
< 0.1%
27 5
 
0.1%
28 7
0.1%
29 6
 
0.1%
30 9
0.1%
31 15
0.2%
ValueCountFrequency (%)
157 1
 
< 0.1%
133 1
 
< 0.1%
132 1
 
< 0.1%
131 1
 
< 0.1%
127 1
 
< 0.1%
125 3
< 0.1%
123 3
< 0.1%
121 2
< 0.1%
119 1
 
< 0.1%
118 2
< 0.1%

저밀도지단백
Real number (ℝ)

Distinct224
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.34686
Minimum1
Maximum1340
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2023-08-17T07:12:04.047271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile62
Q192
median113
Q3136
95-th percentile172
Maximum1340
Range1339
Interquartile range (IQR)44

Descriptive statistics

Standard deviation41.788153
Coefficient of variation (CV)0.36228254
Kurtosis211.83953
Mean115.34686
Median Absolute Deviation (MAD)22
Skewness8.6533564
Sum807428
Variance1746.2497
MonotonicityNot monotonic
2023-08-17T07:12:04.293807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
108 96
 
1.4%
99 95
 
1.4%
107 95
 
1.4%
103 93
 
1.3%
97 92
 
1.3%
110 91
 
1.3%
101 91
 
1.3%
96 89
 
1.3%
121 88
 
1.3%
109 87
 
1.2%
Other values (214) 6083
86.9%
ValueCountFrequency (%)
1 1
 
< 0.1%
12 2
< 0.1%
15 1
 
< 0.1%
16 3
< 0.1%
18 1
 
< 0.1%
19 1
 
< 0.1%
22 1
 
< 0.1%
23 1
 
< 0.1%
24 1
 
< 0.1%
26 3
< 0.1%
ValueCountFrequency (%)
1340 1
< 0.1%
1120 1
< 0.1%
1070 1
< 0.1%
910 1
< 0.1%
590 1
< 0.1%
307 1
< 0.1%
295 1
< 0.1%
293 1
< 0.1%
292 1
< 0.1%
282 1
< 0.1%

헤모글로빈
Real number (ℝ)

Distinct115
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.631914
Minimum4.9
Maximum20.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2023-08-17T07:12:04.537261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4.9
5-th percentile12.2
Q113.6
median14.8
Q315.7
95-th percentile16.805
Maximum20.9
Range16
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation1.5409072
Coefficient of variation (CV)0.10531139
Kurtosis0.98713351
Mean14.631914
Median Absolute Deviation (MAD)1.1
Skewness-0.55880791
Sum102423.4
Variance2.374395
MonotonicityNot monotonic
2023-08-17T07:12:04.783891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.2 202
 
2.9%
15.6 200
 
2.9%
15.3 188
 
2.7%
15 185
 
2.6%
15.7 182
 
2.6%
14.7 182
 
2.6%
15.1 178
 
2.5%
14.9 176
 
2.5%
14.6 174
 
2.5%
15.5 174
 
2.5%
Other values (105) 5159
73.7%
ValueCountFrequency (%)
4.9 1
 
< 0.1%
6.2 1
 
< 0.1%
7.2 1
 
< 0.1%
7.6 1
 
< 0.1%
7.7 1
 
< 0.1%
7.9 2
< 0.1%
8 2
< 0.1%
8.3 3
< 0.1%
8.6 2
< 0.1%
8.7 1
 
< 0.1%
ValueCountFrequency (%)
20.9 1
 
< 0.1%
20 1
 
< 0.1%
19.3 1
 
< 0.1%
19.1 2
< 0.1%
18.8 4
0.1%
18.7 2
< 0.1%
18.6 1
 
< 0.1%
18.5 2
< 0.1%
18.4 2
< 0.1%
18.3 4
0.1%

요 단백
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
1
6618 
2
 
231
3
 
107
4
 
34
5
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 6618
94.5%
2 231
 
3.3%
3 107
 
1.5%
4 34
 
0.5%
5 10
 
0.1%

Length

2023-08-17T07:12:05.042595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-17T07:12:05.257277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 6618
94.5%
2 231
 
3.3%
3 107
 
1.5%
4 34
 
0.5%
5 10
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 6618
94.5%
2 231
 
3.3%
3 107
 
1.5%
4 34
 
0.5%
5 10
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6618
94.5%
2 231
 
3.3%
3 107
 
1.5%
4 34
 
0.5%
5 10
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 7000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6618
94.5%
2 231
 
3.3%
3 107
 
1.5%
4 34
 
0.5%
5 10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6618
94.5%
2 231
 
3.3%
3 107
 
1.5%
4 34
 
0.5%
5 10
 
0.1%

간 효소율
Real number (ℝ)

Distinct238
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1446957
Minimum0.14
Maximum5.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2023-08-17T07:12:05.469669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.14
5-th percentile0.57
Q10.84
median1.1
Q31.38
95-th percentile1.88
Maximum5.67
Range5.53
Interquartile range (IQR)0.54

Descriptive statistics

Standard deviation0.43273479
Coefficient of variation (CV)0.37803478
Kurtosis8.6547245
Mean1.1446957
Median Absolute Deviation (MAD)0.26
Skewness1.6554149
Sum8012.87
Variance0.1872594
MonotonicityNot monotonic
2023-08-17T07:12:05.696041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 284
 
4.1%
1.5 142
 
2.0%
1.33 128
 
1.8%
1.12 112
 
1.6%
0.96 104
 
1.5%
1.2 99
 
1.4%
1.29 98
 
1.4%
1.05 98
 
1.4%
0.88 97
 
1.4%
0.95 97
 
1.4%
Other values (228) 5741
82.0%
ValueCountFrequency (%)
0.14 1
 
< 0.1%
0.29 1
 
< 0.1%
0.31 1
 
< 0.1%
0.32 1
 
< 0.1%
0.34 2
 
< 0.1%
0.36 3
< 0.1%
0.37 3
< 0.1%
0.38 2
 
< 0.1%
0.39 6
0.1%
0.4 2
 
< 0.1%
ValueCountFrequency (%)
5.67 1
 
< 0.1%
5.47 1
 
< 0.1%
5.33 1
 
< 0.1%
5.17 1
 
< 0.1%
4.17 1
 
< 0.1%
4 5
0.1%
3.8 3
< 0.1%
3.59 1
 
< 0.1%
3.52 2
 
< 0.1%
3.5 1
 
< 0.1%

label
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
0
4429 
1
2571 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4429
63.3%
1 2571
36.7%

Length

2023-08-17T07:12:05.915627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-17T07:12:06.140430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 4429
63.3%
1 2571
36.7%

Most occurring characters

ValueCountFrequency (%)
0 4429
63.3%
1 2571
36.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4429
63.3%
1 2571
36.7%

Most occurring scripts

ValueCountFrequency (%)
Common 7000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4429
63.3%
1 2571
36.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4429
63.3%
1 2571
36.7%

Interactions

2023-08-17T07:11:52.081360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:06.799935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:09.731536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:12.867130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:16.832134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:20.482850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:23.475431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:26.589188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:30.531982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:34.355841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:37.369038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:40.389989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:44.277813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:48.880960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:52.295658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:07.014553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:09.936034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:13.076242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:17.170549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:20.714725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:23.712689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:26.809639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:30.847488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:34.567886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:37.580207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:40.611277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:44.631516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:49.113622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:52.515461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:07.221515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:10.131656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:13.296709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:17.511025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:20.928601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:23.933173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:27.009879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:31.175991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:34.775788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:37.778603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:40.817749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:44.974574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:49.327729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:52.736326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:07.420608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:10.329175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:13.499713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:17.806127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:21.135239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:24.141972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:27.207266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:31.473296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:34.976287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:37.978257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:41.038714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:45.270479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:49.541693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:52.944331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:07.624265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:10.523413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:13.787397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:18.096246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:21.328562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:24.357101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:27.409727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:31.806921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:35.199564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:38.198294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:41.266220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:45.607996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:49.773752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:53.151562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:07.812019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:10.899020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:14.100992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:18.397636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:21.560463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:24.586019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:27.608345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:32.149993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:35.403752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:38.411263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:41.478148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:45.935342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:49.981447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:53.364326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:08.021985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:11.140269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:14.440896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:18.611133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:21.772011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:24.817080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:27.850108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:32.463806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:35.617334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:38.637958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:41.714662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:46.348479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:50.214253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:53.602602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:08.254244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:11.362010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:14.770856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:18.828635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:21.997737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:25.048263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:28.079119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:32.807331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:35.840887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:38.854304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:41.939118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:46.698042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:50.447043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:53.825360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:08.454776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:11.558527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:15.085828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:19.023488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:22.205443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:25.250714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:28.285351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:33.120638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:36.056762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:39.067359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:42.147675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:47.013570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:50.685956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:54.034859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:08.648580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:11.760904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:15.394060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:19.217641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:22.404642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:25.465833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:28.614064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:33.311669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:36.264419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:39.283849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:42.369205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:47.355764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:50.926840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:54.237868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:08.854335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:11.971711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:15.671767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:19.424112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:22.629900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:25.677612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:28.891252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:33.515821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:36.478329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:39.508159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:42.928982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:47.694135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:51.160810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:54.459609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:09.079779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:12.195544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:15.977927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:19.642611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:22.846403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:25.923637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:29.214666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:33.732199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:36.723040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:39.733468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:43.202470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:48.020743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:51.391919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:54.692719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:09.303051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:12.420938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:16.230122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:19.860538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:23.063464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:26.138561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:29.559878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:33.938440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:36.943688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:39.939101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:43.570533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:48.384726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:51.640679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:54.907673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:09.525481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:12.654215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:16.554572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:20.084443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:23.269369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:26.375159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:30.233968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:34.147786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:37.171838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:40.176474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:43.932590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:48.667884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-17T07:11:51.884605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-08-17T07:12:06.310582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
나이키(cm)몸무게(kg)BMI시력공복 혈당혈압중성 지방혈청 크레아티닌콜레스테롤고밀도지단백저밀도지단백헤모글로빈간 효소율충치요 단백label
나이1.000-0.500-0.341-0.063-0.3600.2010.1150.032-0.1890.0650.0210.052-0.3240.2130.1220.0330.186
키(cm)-0.5001.0000.7030.1780.2670.0340.0340.1670.483-0.070-0.234-0.0430.571-0.3120.0750.0190.428
몸무게(kg)-0.3410.7031.0000.8040.2020.1730.1410.3630.4360.032-0.3940.0590.544-0.4850.0610.0150.328
BMI-0.0630.1780.8041.0000.0590.2190.1790.3740.2110.104-0.3630.1190.292-0.4240.0200.0000.134
시력-0.3600.2670.2020.0591.000-0.047-0.0610.0420.1200.001-0.0340.0010.195-0.1180.0360.0000.092
공복 혈당0.2010.0340.1730.219-0.0471.0000.1250.2690.0780.056-0.1410.0130.117-0.1810.0000.1020.105
혈압0.1150.0340.1410.179-0.0610.1251.0000.1050.026-0.012-0.054-0.0280.055-0.0740.0000.0310.051
중성 지방0.0320.1670.3630.3740.0420.2690.1051.0000.1650.247-0.4750.0720.303-0.3570.0220.0140.228
혈청 크레아티닌-0.1890.4830.4360.2110.1200.0780.0260.1651.0000.018-0.2250.0510.499-0.2210.0000.1920.129
콜레스테롤0.065-0.0700.0320.1040.0010.056-0.0120.2470.0181.0000.1490.8840.067-0.0930.0160.0000.022
고밀도지단백0.021-0.234-0.394-0.363-0.034-0.141-0.054-0.475-0.2250.1491.000-0.057-0.2840.3420.0000.0130.188
저밀도지단백0.052-0.0430.0590.1190.0010.013-0.0280.0720.0510.884-0.0571.0000.071-0.1000.0000.0000.000
헤모글로빈-0.3240.5710.5440.2920.1950.1170.0550.3030.4990.067-0.2840.0711.000-0.4190.0730.0210.408
간 효소율0.213-0.312-0.485-0.424-0.118-0.181-0.074-0.357-0.221-0.0930.342-0.100-0.4191.0000.0560.0190.191
충치0.1220.0750.0610.0200.0360.0000.0000.0220.0000.0160.0000.0000.0730.0561.0000.0000.097
요 단백0.0330.0190.0150.0000.0000.1020.0310.0140.1920.0000.0130.0000.0210.0190.0001.0000.000
label0.1860.4280.3280.1340.0920.1050.0510.2280.1290.0220.1880.0000.4080.1910.0970.0001.000

Missing values

2023-08-17T07:11:55.228205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-17T07:11:55.775727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ID나이키(cm)몸무게(kg)BMI시력충치공복 혈당혈압중성 지방혈청 크레아티닌콜레스테롤고밀도지단백저밀도지단백헤모글로빈요 단백간 효소율label
0TRAIN_0000351707024.221.1019840801.32117512015.911.531
1TRAIN_0001401505524.441.000173391040.62514618411.811.450
2TRAIN_0002601705017.300.7509640610.8144438915.311.040
3TRAIN_0003401504520.000.5009240460.71786611013.411.180
4TRAIN_0004551556527.061.0008742950.92326215113.811.320
5TRAIN_0005501605521.481.05079531120.72186712813.610.950
6TRAIN_0006601706020.761.00183341670.9168419416.310.840
7TRAIN_0007401757524.491.1008438641.22325016915.710.601
8TRAIN_0008401606023.440.300112642950.72466012713.511.001
9TRAIN_0009451605019.531.1008636580.91714511412.311.330
ID나이키(cm)몸무게(kg)BMI시력충치공복 혈당혈압중성 지방혈청 크레아티닌콜레스테롤고밀도지단백저밀도지단백헤모글로빈요 단백간 효소율label
6990TRAIN_6990501556024.970.80092591610.82185113512.710.930
6991TRAIN_6991451707024.220.6009353401.1152519615.810.970
6992TRAIN_6992551707525.950.7509653910.9136506814.811.070
6993TRAIN_69933517010034.600.400104421441.02093714316.810.611
6994TRAIN_6994401757524.490.80095401130.91845410715.611.271
6995TRAIN_6995251706522.491.50087451411.21844411214.911.500
6996TRAIN_6996601656523.880.9008745820.91846410314.311.471
6997TRAIN_69974018010030.861.2009744870.91785410715.611.000
6998TRAIN_6998601505524.440.60089571610.6157497614.411.000
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